Allow columns to not have a transformer

[npatki]

Problem Description

It should be possible to skip the transformation of select columns by specifying that no transformation is needed in the config.

Expected behavior

• Use None in place of a transformer object to specify that no transformer is needed for the column
• Add new methods remove_transformers and remove_transformers_by_sdtype for better usability in removing transformers from the config
• When a column is marked as None, the column is carried over as-is during the transform call; no changes in value or column name

Additional context

This change will touch many methods.

set_config: Allow the use of None in place of a transformer object

import rdt.transformers as rt

ht = HyperTransformer()
ht.set_config(config={
  'column_A': rt.FloatFormatter(missing_value_replacement=0.00),
  'column_B': None, # don't transform this column
  'column_C': rt.FrequencyEncoder()
})

update_transformers and update_transformers_by_sdtype: Also allow the use of None, though this is not the preferred approach

ht = HyperTransformer()
ht.detect_initial_config(data)

# the following methods will work but will not be mentioned in the docs
ht.update_transformers(column_name_to_transformer={
  'column_B': None
})

ht.update_transformers_by_sdtype(sdtype='categorical', transformer=None)
transformed = ht.fit_transform(data)

remove_transformers and remove_transformers_by_sdtype: The preferred way to remove transformers from the config

ht = HyperTransformer()
ht.detect_initial_config(data)

# remove transformers for the given list of column names
ht.remove_transformers(column_names=['column_B'])

# remove the transformers for everything in the given sdtype
ht.remove_transformers_by_sdtype(sdtype='categorical')

transformed = ht.fit_transform(data)

get_config: Return (and print out) None for the appropriate transformer(s)

{
  'sdtypes': {
    'column_A': 'float',
    'column_B': 'datetime',
    'column_C': 'categorical'
  },
  'transformers': {
    'column_A': FloatFormatter(missing_value_replacement=0.00)
    'column_B': None
    'column_C': FrequencyEncoder()
  }
}

User Validation

1. Invalid sdtype

Check the sdtype the user passes into remove_transformers_by_sdtype

ht.remove_transformers_by_sdtype(sdtype='unkown_type')
Error: Invalid sdtype '<name>'. If you are trying to use a premium sdtype, contact info@sdv.dev about RDT Add-Ons.

2. Invalid column name

ht.remove_transformers(column_names=['column_A', 'unknown_column'])
Error: Invalid column names: ['unknown_column'...]. These columns do not exist in the config. Use 'get_config()' to see the expected values

3. Sdtype & transformer are not compatible

Ignore this case. It does not apply for these functions.

4. User tries to use either of these functions after already calling fit

ht = HyperTransformer()
ht.detect_initial_config(data)
ht.fit()

ht.remove_transformers(column_names=['column_A'])
Warning: For this change to take effect, please refit your data using 'fit' or 'fit_transform'.

ht.remove_transformers_by_sdtype(sdtype='categorical')
Warning: For this change to take effect, please refit your data using 'fit' or 'fit_transform'.